论文标题
COVID-19具有动态和无症状种群的共同流行病学模型
A Generalized Epidemiological Model for COVID-19 with Dynamic and Asymptomatic Population
论文作者
论文摘要
在本文中，我们开发了适用于Covid-19的标准流行病学模型的扩展。由于病毒的症状前或无症状载体，该延伸结合了传播。此外，该模型还捕获了由于一个国家内不同行政界限的人的流动而导致的疾病传播。该模型描述了由于（起步）人类传播和多个隔室的伴随影响，确认病例的数量的概率上升。模型中的相关参数可以帮助架构大流行的公共卫生政策和运营管理。例如，该模型表明，增加对症状患者的测试对大流行的进展没有任何重大影响，但是无症状人群的测试速率具有极其至关重要的作用。该模型是使用印度共和国恰蒂斯加尔邦获得的数据执行的。该模型显示出比其他流行病学模型具有明显更好的预测能力。该模型可以很容易地应用于任何行政边界（州或国家）。此外，该模型也可以用于任何其他流行病。
In this paper, we develop an extension of standard epidemiological models, suitable for COVID-19. This extension incorporates the transmission due to pre-symptomatic or asymptomatic carriers of the virus. Furthermore, this model also captures the spread of the disease due to the movement of people to/from different administrative boundaries within a country. The model describes the probabilistic rise in the number of confirmed cases due to the concomitant effects of (incipient) human transmission and multiple compartments. The associated parameters in the model can help architect the public health policy and operational management of the pandemic. For instance, this model demonstrates that increasing the testing for symptomatic patients does not have any major effect on the progression of the pandemic, but testing rate of the asymptomatic population has an extremely crucial role to play. The model is executed using the data obtained for the state of Chhattisgarh in the Republic of India. The model is shown to have significantly better predictive capability than the other epidemiological models. This model can be readily applied to any administrative boundary (state or country). Moreover, this model can be applied for any other epidemic as well.
